OpenRaiser/Intern-Atlas huggingface dataset:
RAIDER: Tool-Equipped Large Language Model Agent for Robotic Action Issue Detection, Explanation and Recovery
Venue: arXiv cs.RO 2025
Year: 2025

As robots increasingly operate in dynamic human-centric environments, improving their ability to detect, explain, and recover from action-related issues becomes crucial. Traditional model-based and data-driven techniques lack adaptability, while more flexible generative AI methods struggle with grounding extracted information to real-world constraints. We introduce RAIDER, a novel agent that integrates Large Language Models (LLMs) with grounded tools for adaptable and efficient issue detection and explanation. Using a unique "Ground, Ask&Answer, Issue" procedure, RAIDER dynamically generates context-aware precondition questions and selects appropriate tools for resolution, achieving targeted information gathering. Our results within a simulated household environment surpass methods relying on predefined models, full scene descriptions, or standalone trained models. Additionally, RAIDER's explanations enhance recovery success, including cases requiring human interaction. Its modular architecture, featuring self-correction mechanisms, enables straightforward adaptation to diverse scenarios, as demonstrated in a real-world human-assistive task. This showcases RAIDER's potential as a versatile agentic AI solution for robotic issue detection and explanation, while addressing the problem of grounding generative AI for its effective application in embodied agents. Project website: https://eurecat.github.io/raider-llmagent/
Multi-modality Anomaly Segmentation on the Road
Venue: arXiv cs.CV 2025
Year: 2025

Semantic segmentation allows autonomous driving cars to understand the surroundings of the vehicle comprehensively. However, it is also crucial for the model to detect obstacles that may jeopardize the safety of autonomous driving systems. Based on our experiments, we find that current uni-modal anomaly segmentation frameworks tend to produce high anomaly scores for non-anomalous regions in images. Motivated by this empirical finding, we develop a multi-modal uncertainty-based anomaly segmentation framework, named MMRAS+, for autonomous driving systems. MMRAS+ effectively reduces the high anomaly outputs of non-anomalous classes by introducing text-modal using the CLIP text encoder. Indeed, MMRAS+ is the first multi-modal anomaly segmentation solution for autonomous driving. Moreover, we develop an ensemble module to further boost the anomaly segmentation performance. Experiments on RoadAnomaly, SMIYC, and Fishyscapes validation datasets demonstrate the superior performance of our method. The code is available in https://github.com/HengGao12/MMRAS_plus.
Trigger without Trace: Towards Stealthy Backdoor Attack on Text-to-Image Diffusion Models
Venue: arXiv cs.CV 2025
Year: 2025

Backdoor attacks targeting text-to-image diffusion models have advanced rapidly. However, current backdoor samples often exhibit two key abnormalities compared to benign samples: 1) Semantic Consistency, where backdoor prompts tend to generate images with similar semantic content even with significant textual variations to the prompts; 2) Attention Consistency, where the trigger induces consistent structural responses in the cross-attention maps. These consistencies leave detectable traces for defenders, making backdoors easier to identify. In this paper, toward stealthy backdoor samples, we propose Trigger without Trace (TwT) by explicitly mitigating these consistencies. Specifically, our approach leverages syntactic structures as backdoor triggers to amplify the sensitivity to textual variations, effectively breaking down the semantic consistency. Besides, a regularization method based on Kernel Maximum Mean Discrepancy (KMMD) is proposed to align the distribution of cross-attention responses between backdoor and benign samples, thereby disrupting attention consistency. Extensive experiments demonstrate that our method achieves a 97.5% attack success rate while exhibiting stronger resistance to defenses. It achieves an average of over 98% backdoor samples bypassing three state-of-the-art detection mechanisms, revealing the vulnerabilities of current backdoor defense methods. The code is available at https://github.com/Robin-WZQ/TwT.
RDTF: Resource-efficient Dual-mask Training Framework for Multi-frame Animated Sticker Generation
Venue: arXiv cs.MM 2025
Year: 2025

Recently, significant advancements have been achieved in video generation technology, but applying it to resource-constrained downstream tasks like multi-frame animated sticker generation (ASG) characterized by low frame rates, abstract semantics, and long tail frame length distribution-remains challenging. Parameter-efficient fine-tuning (PEFT) techniques (e.g., Adapter, LoRA) for large pre-trained models suffer from insufficient fitting ability and source-domain knowledge interference. In this paper, we propose Resource-Efficient Dual-Mask Training Framework (RDTF), a dedicated solution for multi-frame ASG task under resource constraints. We argue that training a compact model from scratch with million-level samples outperforms PEFT on large models, with RDTF realizing this via three core designs: 1) a Discrete Frame Generation Network (DFGN) optimized for low-frame-rate ASG, ensuring parameter efficiency; 2) a dual-mask based data utilization strategy to enhance the availability and diversity of limited data; 3) a difficulty-adaptive curriculum learning method that decomposes sample entropy into static and adaptive components, enabling easy-to-difficult training convergence. To provide high-quality data support for RDTFs training from scratch, we construct VSD2M-a million-level multi-modal animated sticker dataset with rich annotations (static and animated stickers, action-focused text descriptions)-filling the gap of dedicated animated data for ASG task. Experiments demonstrate that RDTF is quantitatively and qualitatively superior to state-of-the-art PEFT methods (e.g., I2V-Adapter, SimDA) on ASG tasks, verifying the feasibility of our framework under resource constraints.
Topology preserving Image segmentation using the iterative convolution-thresholding method
Venue: arXiv cs.CV 2025
Year: 2025

Variational models are widely used in image segmentation, with various models designed to address different types of images by optimizing specific objective functionals. However, traditional segmentation models primarily focus on the visual attributes of the image, often neglecting the topological properties of the target objects. This limitation can lead to segmentation results that deviate from the ground truth, particularly in images with complex topological structures. In this paper, we introduce a topology-preserving constraint into the iterative convolution-thresholding method (ICTM), resulting in the topology-preserving ICTM (TP-ICTM). Extensive experiments demonstrate that, by explicitly preserving the topological properties of target objects-such as connectivity-the proposed algorithm achieves enhanced accuracy and robustness, particularly in images with intricate structures or noise.
Relation Extraction with Instance-Adapted Predicate Descriptions
Venue: arXiv cs.CL 2025
Year: 2025

Relation extraction (RE) is a standard information extraction task playing a major role in downstream applications such as knowledge discovery and question answering. Although decoder-only large language models are excelling in generative tasks, smaller encoder models are still the go to architecture for RE. In this paper, we revisit fine-tuning such smaller models using a novel dual-encoder architecture with a joint contrastive and cross-entropy loss. Unlike previous methods that employ a fixed linear layer for predicate representations, our approach uses a second encoder to compute instance-specific predicate representations by infusing them with real entity spans from corresponding input instances. We conducted experiments on two biomedical RE datasets and two general domain datasets. Our approach achieved F1 score improvements ranging from 1% to 2% over state-of-the-art methods with a simple but elegant formulation. Ablation studies justify the importance of various components built into the proposed architecture.
Neural Network Approach to Stochastic Dynamics for Smooth Multimodal Density Estimation
Venue: arXiv cs.LG 2025
Year: 2025

In this paper we consider a new probability sampling methods based on Langevin diffusion dynamics to resolve the problem of existing Monte Carlo algorithms when draw samples from high dimensional target densities. We extent Metropolis-Adjusted Langevin Diffusion algorithm by modelling the stochasticity of precondition matrix as a random matrix. An advantage compared to other proposal method is that it only requires the gradient of log-posterior. The proposed method provides fully adaptation mechanisms to tune proposal densities to exploits and adapts the geometry of local structures of statistical models. We clarify the benefits of the new proposal by modelling a Quantum Probability Density Functions of a free particle in a plane (energy Eigen-functions). The proposed model represents a remarkable improvement in terms of performance accuracy and computational time over standard MCMC method.
Measuring Vogue in American Sociology (2011-2020)
Venue: arXiv cs.SI 2025
Year: 2025

This study investigates the social dynamics of knowledge production in American sociology. Departing from traditional approaches focused on citations, co-authorship, and faculty hiring, we introduce a method capturing the dynamics of networks inferred from text to explore which ideas gain traction (a.k.a vogue). Drawing on sociology doctoral dissertations and journal abstracts, we trace the movement of word pairs between peripheral and core semantic networks to uncover dominant themes and methodological trajectories. Our findings demonstrate that regional location and institutional prestige play critical roles in shaping the production and adoption of research trends across 114 sociology PhD-granting institutions in the United States. We show that applied research topics, such as crime and health, despite being perceived as less prestigious than theoretically oriented subjects, serve as the primary driving force behind the emergence and diffusion of trends within the discipline. This work sheds light on the institutional mechanisms that govern knowledge production, demonstrating that sociology's intellectual landscape is not dictated by simple top-down diffusion from elite institutions but is instead structured by the contextual and institutional factors that facilitate specialization and segmentation.
A novel gradient-based method for decision trees optimizing arbitrary differential loss functions
Venue: arXiv cs.LG 2025
Year: 2025

There are many approaches for training decision trees. This work introduces a novel gradient-based method for constructing decision trees that optimize arbitrary differentiable loss functions, overcoming the limitations of heuristic splitting rules. Unlike traditional approaches that rely on heuristic splitting rules, the proposed method refines predictions using the first and second derivatives of the loss function, enabling the optimization of complex tasks such as classification, regression, and survival analysis. We demonstrate the method's applicability to classification, regression, and survival analysis tasks, including those with censored data. Numerical experiments on both real and synthetic datasets compare the proposed method with traditional decision tree algorithms, such as CART, Extremely Randomized Trees, and SurvTree. The implementation of the method is publicly available, providing a practical tool for researchers and practitioners. This work advances the field of decision tree-based modeling, offering a more flexible and accurate approach for handling structured data and complex tasks. By leveraging gradient-based optimization, the proposed method bridges the gap between traditional decision trees and modern machine learning techniques, paving the way for further innovations in interpretable and high-performing models.
Architectural and System Implications of CXL-enabled Tiered Memory
Venue: arXiv cs.AR 2025
Year: 2025

Memory disaggregation is an emerging technology that decouples memory from traditional memory buses, enabling independent scaling of compute and memory. Compute Express Link (CXL), an open-standard interconnect technology, facilitates memory disaggregation by allowing processors to access remote memory through the PCIe bus while preserving the shared-memory programming model. This innovation creates a tiered memory architecture combining local DDR and remote CXL memory with distinct performance characteristics. In this paper, we investigate the architectural implications of CXL memory, focusing on its increased latency and performance heterogeneity, which can undermine the efficiency of existing processor designs optimized for (relatively) uniform memory latency. Using carefully designed micro-benchmarks, we identify bottlenecks such as limited hardware-level parallelism in CXL memory, unfair queuing in memory request handling, and its impact on DDR memory performance and inter-core synchronization. Our findings reveal that the disparity in memory tier parallelism can reduce DDR memory bandwidth by up to 81% under heavy loads. To address these challenges, we propose a Dynamic Memory Request Control mechanism, MIKU, that prioritizes DDR memory requests while serving CXL memory requests on a best-effort basis. By dynamically adjusting CXL request rates based on service time estimates, MIKU achieves near-peak DDR throughput while maintaining high performance for CXL memory. Our evaluation with micro-benchmarks and representative workloads demonstrates the potential of MIKU to enhance tiered memory system efficiency.
Think Before Refusal : Triggering Safety Reflection in LLMs to Mitigate False Refusal Behavior
Venue: arXiv cs.CL 2025
Year: 2025

Recent advancements in large language models (LLMs) have demonstrated that fine-tuning and human alignment can render LLMs harmless. In practice, such "harmlessness" behavior is mainly achieved by training models to reject harmful requests, such as "Explain how to burn down my neighbor's house", where the model appropriately declines to respond. However, this approach can inadvertently result in false refusal, where models reject benign queries as well, such as "Tell me how to kill a Python process". In this work, we demonstrate that prompting safety reflection before generating a response can mitigate false refusal behavior. Building on this finding, we introduce the Think-Before-Refusal (TBR) schema and conduct safety-aware instruction fine-tuning incorporating safety reflection. In an ablation study across 15 pre-trained models, we show that models fine-tuned with safety reflection significantly reduce false refusal behavior while maintaining safety and overall performance compared to those fine-tuned without safety reflection.
What Time Tells Us? An Explorative Study of Time Awareness Learned from Static Images
Venue: arXiv cs.CV 2025
Year: 2025

Time becomes visible through illumination changes in what we see. Inspired by this, in this paper we explore the potential to learn time awareness from static images, trying to answer: *what time tells us?* To this end, we first introduce a Time-Oriented Collection (TOC) dataset, which contains 130,906 images with reliable timestamps. Leveraging this dataset, we propose a Time-Image Contrastive Learning (TICL) approach to jointly model timestamps and related visual representations through cross-modal contrastive learning. We found that the proposed TICL, 1) not only achieves state-of-the-art performance on the timestamp estimation task, over various benchmark metrics, 2) but also, interestingly, though only seeing static images, the time-aware embeddings learned from TICL show strong capability in several time-aware downstream tasks such as time-based image retrieval, video scene classification, and time-aware image editing. Our findings suggest that time-related visual cues can be learned from static images and are beneficial for various vision tasks, laying a foundation for future research on understanding time-related visual context. Project page: https://rathgrith.github.io/timetells_release/
Guided Diffusion for the Extension of Machine Vision to Human Visual Perception
Venue: arXiv cs.CV 2025
Year: 2025

Image compression technology eliminates redundant information to enable efficient transmission and storage of images, serving both machine vision and human visual perception. For years, image coding focused on human perception has been well-studied, leading to the development of various image compression standards. On the other hand, with the rapid advancements in image recognition models, image compression for AI tasks, known as Image Coding for Machines (ICM), has gained significant importance. Therefore, scalable image coding techniques that address the needs of both machines and humans have become a key area of interest. Additionally, there is increasing demand for research applying the diffusion model, which can generate human-viewable images from a small amount of data to image compression methods for human vision. Image compression methods that use diffusion models can partially reconstruct the target image by guiding the generation process with a small amount of conditioning information. Inspired by the diffusion model's potential, we propose a method for extending machine vision to human visual perception using guided diffusion. Utilizing the diffusion model guided by the output of the ICM method, we generate images for human perception from random noise. Guided diffusion acts as a bridge between machine vision and human vision, enabling transitions between them without any additional bitrate overhead. The generated images then evaluated based on bitrate and image quality, and we compare their compression performance with other scalable image coding methods for humans and machines.
Cache-Aware Cooperative Multicast Beamforming in Dynamic Satellite-Terrestrial Networks
Venue: arXiv cs.NI 2025
Year: 2025

With the burgeoning demand for data-intensive services, satellite-terrestrial networks (STNs) face increasing backhaul link congestion, deteriorating user quality of service (QoS), and escalating power consumption. Cache-aided STNs are acknowledged as a promising paradigm for accelerating content delivery to users and alleviating the load of backhaul links. However, the dynamic nature of low earth orbit (LEO) satellites and the complex interference among satellite beams and terrestrial base stations pose challenges in effectively managing limited edge resources. To address these issues, this paper proposes a method for dynamically scheduling caching and communication resources, aiming to reduce network costs in terms of transmission power consumption and backhaul traffic, while meeting user QoS demands and resource constraints. We formulate a mixed timescale problem to jointly optimize cache placement, LEO satellite beam direction, and cooperative multicast beamforming among satellite beams and base stations. To tackle this intricate problem, we propose a two-stage solution framework, where the primary problem is decoupled into a short-term content delivery subproblem and a long-term cache placement subproblem. The former subproblem is solved by designing an alternating optimization approach with whale optimization and successive convex approximation methods according to the cache placement state, while cache content in STNs is updated using an iterative algorithm that utilizes historical information. Simulation results demonstrate the effectiveness of our proposed algorithms, showcasing their convergence and significantly reducing transmission power consumption and backhaul traffic by up to 52%.
SLIDE: Sliding Localized Information for Document Extraction
Venue: arXiv cs.CL 2025
Year: 2025

Constructing accurate knowledge graphs from long texts and low-resource languages is challenging, as large language models (LLMs) experience degraded performance with longer input chunks. This problem is amplified in low-resource settings where data scarcity hinders accurate entity and relationship extraction. Contextual retrieval methods, while improving retrieval accuracy, struggle with long documents. They truncate critical information in texts exceeding maximum context lengths of LLMs, significantly limiting knowledge graph construction. We introduce SLIDE (Sliding Localized Information for Document Extraction), a chunking method that processes long documents by generating local context through overlapping windows. SLIDE ensures that essential contextual information is retained, enhancing knowledge graph extraction from documents exceeding LLM context limits. It significantly improves GraphRAG performance, achieving a 24% increase in entity extraction and a 39% improvement in relationship extraction for English. For Afrikaans, a low-resource language, SLIDE achieves a 49% increase in entity extraction and an 82% improvement in relationship extraction. Furthermore, it improves upon state-of-the-art in question-answering metrics such as comprehensiveness, diversity and empowerment, demonstrating its effectiveness in multilingual and resource-constrained settings.
Dynamic Gradient Sparse Update for Edge Training
Venue: arXiv cs.AR 2025
Year: 2025

Training on edge devices enables personalized model fine-tuning to enhance real-world performance and maintain data privacy. However, the gradient computation for backpropagation in the training requires significant memory buffers to store intermediate features and compute losses. This is unacceptable for memory-constrained edge devices such as microcontrollers. To tackle this issue, we propose a training acceleration method using dynamic gradient sparse updates. This method updates the important channels and layers only and skips gradient computation for the less important channels and layers to reduce memory usage for each update iteration. In addition, the channel selection is dynamic for different iterations to traverse most of the parameters in the update layers along the time dimension for better performance. The experimental result shows that the proposed method enables an ImageNet pre-trained MobileNetV2 trained on CIFAR-10 to achieve an accuracy of 85.77\% while updating only 2\% of convolution weights within 256KB on-chip memory. This results in a remarkable 98\% reduction in feature memory usage compared to dense model training.